Workshops

LATIS offers a series of workshops created by our experts that are free and open to all faculty and graduate students. Join our LATIS Research Workshops Google Group to be the first to learn about workshops. Joining the group is highly recommended as these workshops are popular and often fill to capacity. You can view the slides and materials from past workshops at the LATIS Workshop Materials website.

Fall 2019 Workshops

Workshops are held Fridays from 10am-12:30pm

Introduction Nvivo - Sept 20

Introduction to R - Sept 27

Reproducible Research Workflows - Oct 4

Introduction to Python [for social data science] - Oct 18

Introduction to Web APIs in Python  - Nov 1

Introduction to Web Scraping - Nov 15

Reproducible Experimental Design in Qualtrics - Nov 22

 

Workshop descriptions

 

Introduction to R

Register here for the workshop.

R is a popular tool for data analysis and statistical computing, and is a great alternative to tools like SPSS, Stata, or Excel. R is free and designed for reproducible research. This workshop will teach you how to get started using R to explore and clean your data. We will focus on issues social scientists often encounter when using data in R. 

This workshop will cover how to:

  • Create an R script (syntax/command file) to capture data cleaning steps in a reproducible way
  • Load a comma-delimited spreadsheet (.csv) into R as a dataset
  • View and examine data in R 
  • Check and correct missing values, rename variables, create new variables, and recode values in the data 
  • Save cleaned data file in formats for later use in R or other applications

To be successful, you should have:

 

Introduction to Python for Social Science

Register here for the workshop.

Python has seen wide adoption in academic research because it is a powerful but easy-to-learn programming language. It can be used in a manner similar to R or Stata for statistical processing, but also provides wider application in data processing, collection, and file management. Python is free and can be used in many phases of a project to enhance the reproducibility of research. This workshop will teach you how to get started using Python and some of its basic syntax, grammar and structures. It will also introduce the popular package Pandas which provides a familiar dataframe structure to import, format, and clean data as well as functions to manipulate, filter, and analyze data.

This workshop will cover how to:
  • Use Python 3 in a JupyterLab computing environment
  • Create an script (syntax/command file) to capture steps in a reproducible way
  • Use Python to grab data from a large number of files quickly
  • Load a comma-delimited spreadsheet (.csv) into Pandas as a dataframe
  • View and clean that data
  • Save cleaned data file in formats for later use

To be successful, you should have:

  • A familiarity with data used in the social sciences
  • A familiarity with another statistical or data processing tool, such as R, SPSS, Stata, SAS, or Excel
  • A laptop you can bring to the workshop

 

Introduction to NVivo

Register here for the workshop.

NVivo is a qualitative data management, coding and markup tool, that facilitates powerful querying and exploration of source materials for both mixed methods and qualitative analysis. It integrates well with tools that assist in data collection and can handle a wide variety of source materials. This workshop introduces the basic functions of NVivo, with no prior experience necessary. The session is held in a computer lab with the software already installed. (Licensing is provided for faculty and graduate students of the College of Liberal Arts and the Humphrey School of Public Affairs.) 

This workshop will cover

  • Adding your source materials (text, images, audio/video, survey/spreadsheets)
  • Working with concepts (or codes/tags) and their definitions
  • Making annotations and analytical memos
  • Using text queries to speed up coding
  • Finding patterns in the concepts identified in the source materials
  • Importing data from other tools including Qualtrics, OneNote, and Zotero
  • Exporting excerpts and making backups
  • Working in teams

To be successful, you should

  • Be familiar with source materials used in qualitative research (interviews, focus groups, field notes, archival documents, etc.)
  • Be familiar with the types of questions asked in qualitative research

 

Introduction to Web APIs in Python

Register here for the workshop.

Web APIs (Application Programming Interfaces) provide a way for scholars to efficiently and legally access and download data from web platforms and publications such as Twitter and the New York Times. In this workshop we’ll use Python to query and download data using the NY Times API.

This workshop will cover how to:

  • Use Python 3 in a JupyterLab computing environment
  • Read API documentation to build successful API queries
  • Use the Requests and JSON Python libraries to download data from the NY Times API
  • Use built-in Python functions such as type, len, and dir to explore API data
  • Explore API data in Python using dictionaries

To be successful, you should have:

  • A laptop you can bring to the workshop, with 
  • Anaconda installed for using Python
  • Create a free NY Times developer account 
  • No prior experience with these tools is necessary, and participants do not need to have any coding skills. 
  • The Introduction to Python workshop on October 18, 2019 is not required, but recommended.


 

Introduction to Web Scraping

Register here for the workshop.

The internet is full of information waiting for exploration - from social media, to newspaper comments, to digitized archives. How do you begin gathering this kind of data? This workshop will introduce participants to browser-based tools for web scraping as well as reproducible web scraping methods using Python. We will cover essential legal literacies to ensure you can make informed decisions about when and how to web scrape following legal and ethical best practices.

This workshop will cover how to:

  • View and explore the HTML tree underlying every webpage you see
  • Use a browser extension (Scraper) to systematically copy sections of matching HTML from a single webpage
  • Use Python 3 in a JupyterLab computing environment
  • Use the Requests and BeautifulSoup Python libraries to access HTML data from the web
  • Create variables, lists and loops to work with web data in Python
  • Store and view HTML data in Pandas dataframe format

To be successful, you should have:

  • A laptop you can bring to the workshop, with the following installed:
  • Anaconda for using Python in JupyterLab
  • Scraper Chrome extension
  • If you have any installation issues, feel free to arrive at 9:30 for help troubleshooting.
  • No prior experience with these tools is necessary, and participants do not need to have any coding skills. 
  • The Introduction to Python workshop on October 18, 2019 is not required, but recommended.

 

Reproducible Research Workflows

Register here for the workshop.

From addressing the "replication crisis" to saving hours of work reconstructing a past project, adopting a more reproducible research workflow can have substantial benefits. This workshop will introduce the concept of reproducibility, discuss components of transparent research workflows, and provide an overview of methods and tools you can incorporate into your work. 

This workshop will cover how to:

  • Understand the various components of reproducibility and identify which are most relevant for your individual research 
  • Examine current workflows and identify pieces that could benefit from more reproducible practices
  • Navigate the many tools and techniques available for automating, documenting, and sharing research

To be successful, you should have:

  • A project or research workflow you would like to examine and make more reproducible
  • An interest in learning about tools, methods, and resources for building reproducible workflows

 

Reproducible Experimental Design in Qualtrics

Register here for the workshop.

Qualtrics is a versatile data collection tool that is available to all University researchers, and it can be used for a wide range of survey and experimental needs. However, finding the right bells and whistles when using this tool for your research can be daunting. This workshop will teach you how to develop online experiments using this tool and introduce best practices for reproducibility & data management so that your future self loves what you did.

This workshop will cover how to:

  • Capture important survey metadata
  • Edit metadata information (i.e., recode values, variable names, question labels, etc.) within Qualtrics to maximize data management efficiency 
  • Randomize participants to conditions, customize participant survey/task paths based on responses, and embed multimedia stimuli into Qualtrics instruments
  • Create reproducible question flows with Qualtrics’ “Loop & Merge” tool
  • Integrate Qualtrics with other online tools, such as Amazon’s Mechanical Turk, or other surveys

To be successful, you should have: